1. Field of the Invention
The present invention relates to the predictive coding of information signals, such as, for example, audio signals, and in particular to adaptive predictive coding.
2. Description of the Related Art
predictive coder—or transmitter—codes signals by predicting a current value of the signal to be coded by the previous or preceding values of the signal. In the case of linear prediction, this prediction or presumption is accomplished via the current value of the signal by a weighted sum of the previous values of the signal. The prediction weights or prediction coefficients are continuously adjusted or adapted to the signal so that the difference between the predicted signal and the actual signal is minimized in a predetermined manner. The prediction coefficients, for example, are optimized with regard to the square of the prediction error. The error criterion when optimizing the predictive coder or predictor, however, may also be selected to be something else. Instead of using the least square error criterion, the spectral flatness of the error signal, i.e. of the differences or residuals, may be minimized.
Only the differences between the predicted values and the actual values of the signal are transmitted to the decoder or receiver. These values are referred to as residuals or prediction errors. The actual signal value can be reconstructed in the receiver by using the same predictor and by adding the predicted value obtained in the same manner as in the coder to the prediction error having been transmitted by the coder.
The prediction weights for the prediction may be adapted to the signal with a predetermined speed. In the so-called least mean squares (LMS) algorithm, one parameter is used for this. The parameter must be adjusted in a manner acting as a trade-off between adaption speed and precision of the prediction coefficients. This parameter, which is sometimes also referred to as step-size parameter, thus determines how fast the prediction coefficients adapt to an optimum set of prediction coefficients, wherein a set of prediction coefficients not adjusted optimally results in the prediction to be less precise and thus the prediction errors to be greater, which in turn results in an increased bit rate for transmitting the signal since small values or small prediction errors or differences can be transmitted by fewer bits than greater ones.
A problem in predictive coding is that in the case of transmitting errors, i.e. if incorrectly transmitted prediction differences or errors occur, prediction will no longer be the same on the transmitter and receiver sides. Incorrect values will be reconstructed since, when a prediction error first occurs, it is added on the receiver side to the currently predicted value to obtain the decoded value of the signal. Subsequent values, too, are affected since the prediction on the receiver side is performed based on the signal values already decoded.
In order to obtain resynchronization or adjustment between transmitter and receiver, the predictors, i.e. the prediction algorithms, are reset to a certain state on the transmitter and receiver sides at predetermined times equal for both sides, a process also referred to as reset.
However, it is problematic that directly after such a reset the prediction coefficients are not adjusted to the signal at all. The adaption of these prediction coefficients, however, will always require some time starting from the reset times. This increases the mean prediction error resulting in an increased bit rate or reduced signal quality, such as, for example, due to distortions.